Evaluating Regression Models

Oct 19, 2013

280 likes | 505 Views

Good Models and Explaining" Variance. What do we mean when we say a model is good" or explains" the dependent variable?Explanation exists in our theory, not in any data we might observe.Thus explained" variance cannot be measured with a statistic (or with data). What do we mean when we say we

Share Presentation

Embed Code

Link

monisha

Download Presentation

Evaluating Regression Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Multilevel Regression Models

Multilevel Regression Models . sean f. reardon 17 june, 2004. Outline. What/Why Multilevel Regression? The Basic Multilevel Regression Model Growth Models A Taste of Advanced Topics. Part I.A. What are multilevel data and multilevel analysis?.

2.36k views • 147 slides

Regression Models

Regression Models. Chapter 4. To accompany Quantitative Analysis for Management , Tenth Edition , by Render, Stair, and Hanna Power Point slides created by Jeff Heyl. © 2009 Prentice-Hall, Inc. . Learning Objectives. After completing this chapter, students will be able to:.

1.45k views • 71 slides

Linear Regression Models

Linear Regression Models. Based on Chapter 3 of Hastie, Tibshirani and Friedman Slides by David Madigan. Linear Regression Models. Here the X ’s might be:. Raw predictor variables (continuous or coded-categorical) Transformed predictors ( X 4 =log X 3 )

378 views • 19 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 8 – Multicollinearity, Diagnostics. Multiple Regression Models. Multicollinearity

596 views • 35 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 7 – Multiple Regression Analysis. Model Assumptions.

763 views • 54 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 3 – Model Fit and Correlation. Correlation and Linear Association. Height (inches) and Income ($/mo.) in first post-MBA

189 views • 0 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models . Part 5 – Elasticities and Functional Form. Linear Regression Models. Model building Linear models – cost functions

780 views • 44 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Statistics and Data Analysis. Part 10 – Qualitative Data. Modeling Qualitative Data. A Binary Outcome Yes or No – Bernoulli Survey Responses: Preference Scales

335 views • 21 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 1 – Simple Linear Model. Theory. Demand Theory: Q = f(Price) “The Law of Demand” Demand curves slope downward

542 views • 30 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models . Part 4 – Prediction. Prediction. Use of the model for prediction Use “x” to predict y based on y = β 0 + β 1 x + ε Sources of uncertainty

383 views • 22 slides

LINEAR REGRESSION: Evaluating Regression Models

LINEAR REGRESSION: Evaluating Regression Models. Overview. Standard Error of the Estimate Goodness of Fit Coefficient of Determination Regression Coefficients. Standard Error of the Estimate. Index of how far off predictions are expected to be Larger r means smaller standard error

476 views • 19 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 6 – Multiple Regression. Multiple Regression Agenda. The concept of multiple regression Computing the regression equation

798 views • 45 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 2 – Inference About the Regression. The Linear Regression Model. 1. The linear regression model

702 views • 49 slides

Regression Models

Regression Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 9 – Model Building. Multiple Regression Models. Using Binary Variables Logs and Elasticities Trends in Time Series Data

628 views • 43 slides

Regression Models

Regression Models. Fit data Time-series data: Forecast Other data: Predict. Use in Data Mining. One of major analytic models Linear regression The standard – ordinary least squares regression Can use for discriminant analysis Can apply stepwise regression

429 views • 25 slides

Linear regression models

Linear regression models. Simple Linear Regression. History. Developed by Sir Francis Galton (1822-1911) in his article “Regression towards mediocrity in hereditary structure”. Purposes:.

638 views • 31 slides

Nonlinear Regression Models

Nonlinear Regression Models. Nonlinear Regression Models. Lets revisit the AR(1) model of autocorrelation: y t = x t β +e t [ i ] e t = ρ e t-1 + ν t → ν t = e t - ρ e t-1 ρ y t-1 = ρ x t-1 β + ρ e t-1 (t=2,3,…,T) → y t − ρ y t-1 = x t β − ρ x t-1 β +e t - ρ e t-1

4.28k views • 139 slides

Regression Models

Population Deterministic Regression Model Y i =  0 +  1 X i Y i only depends on the value of X i and no other factor can affect Y i . Population Probabilistic Regression Model Y i =  0 +  1 X i +  i , i = 1, 2, ... , n. E(Y |X i )=  0 +  1 X i ,

436 views • 33 slides

Multiple Regression Models

Multiple Regression Models. Exploring an example: “Chapter 4: Multiple Regression II” data.

498 views • 28 slides

Evaluating the Uncertainty of Land-Use Regression Models

Halûk Özkaynak US EPA, Office of Research and Development National Exposure Research Laboratory, RTP, NC Presented at the CMAS Special Symposium on Air Quality October 13, 2010. Evaluating the Uncertainty of Land-Use Regression Models. Land-Use Regression (LUR) Models.

348 views • 23 slides

Regression Models

Regression Models. Chapter 4. Learning Objectives. After completing this chapter, students will be able to:. Identify variables and use them in a regression model Develop simple linear regression equations from sample data and interpret the slope and intercept

757 views • 71 slides

Regression Models

Regression Models. Residuals and Diagnosing the Quality of a Model. Visualizing Regression Models. Criteria of quality. Residuals (or what we don’t explain) should be “noise” Independent variables measure different phenomena We haven’t left out something important.

267 views • 24 slides

More Related